[NFC] Convert openmp/runtime/tools/summarizeStats.py to python 3 syntax

[thieta]

Summary

I found a few python scripts still using python 2 syntax in the tree.

This has been converted with 2to3 and the changes seems sane, but
I don't know how to use this script so if someone knows how to test
it, that would probably be good to run.

[serge-sans-paille]

Some extra call to list are correct wrt py2/py3 compat but are actually not needed, let's not introduce them at all.

openmp/runtime/tools/summarizeStats.py

175

I think this list call here is actually not needed.

195

same here

209

here I'm unsure.

217

not needed

230

not neeed

306

not needed

[thieta]

Removed unecessary lists

[thieta]

Thanks for the review - removed those lists(). Wonder why 2to3 is so aggressive with inserting them? anyway - it seems to parse fine afterwards. but since I don't know the files this should be executed on it's hard for me to test the functionallity.

[thieta]

Are you good with this now @serge-sans-paille ?

[jlpeyton]

openmp/runtime/tools/summarizeStats.py

306

I do think this list() is necessary because the del data[key] line may change the size of the dictionary during iteration.

[serge-sans-paille]

@thieta ACK once you fix the issue spotted by@jlpeyton.

openmp/runtime/tools/summarizeStats.py

306

Correct! my bad, thanks @jlpeyton for spotting this.

openmp/runtime/tools/summarizeStats.py

def draw_poly_frame(self, x0, y0, r):
         verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]
         return plt.Polygon(verts, closed=True, edgecolor='k')
 
     def draw_circle_frame(self, x0, y0, r):
         return plt.Circle((x0, y0), r)
 
     frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame}
     if frame not in frame_dict:
-        raise ValueError, 'unknown value for `frame`: %s' % frame
+        raise ValueError('unknown value for `frame`: %s' % frame)
 
     class RadarAxes(PolarAxes):
         """
         Class for creating a radar chart (a.k.a. a spider or star chart)
 
         http://en.wikipedia.org/wiki/Radar_chart
         """
         name = 'radar'
def readFile(fname):
     """Read the statistics from the file. Return a dict with keys "timers", "counters" """
     res = {}
     try:
         with open(fname) as f:
             res["timers"]   = readTimers(f)
             res["counters"] = readCounters(f)
             return res
     except (OSError, IOError):
-        print "Cannot open " + fname
+        print("Cannot open " + fname)
         return None
 
 def usefulValues(l):
     """I.e. values which are neither null nor zero"""
     return [p and q for (p,q) in zip (pd.notnull(l), l != 0.0)]
 
 def uselessValues(l):
     """I.e. values which are null or zero"""
def drawChart(data, kind, filebase):
     plt.ylabel(statProperties[kind][0])
     plt.title (statProperties[kind][1])
     plt.tight_layout()
     plt.savefig(filebase+"_"+kind)
 
 def normalizeValues(data, countField, factor):
     """Normalize values into a rate by dividing them all by the given factor"""
     data[[k for k in data.keys() if k != countField]] /= factor
 
 
 def setRadarFigure(titles):
     """Set the attributes for the radar plots"""
     fig = plt.figure(figsize=(9,9))
     rect = [0.1, 0.1, 0.8, 0.8]
     labels = [0.2, 0.4, 0.6, 0.8, 1, 2, 3, 4, 5, 10]
     matplotlib.rcParams.update({'font.size':13})
     theta = radar_factory(len(titles))
     ax = fig.add_axes(rect, projection='radar')
     ax.set_rgrids(labels)
     ax.set_varlabels(titles)
     ax.text(theta[2], 1, "Linear->Log", horizontalalignment='center', color='green', fontsize=18)
     return {'ax':ax, 'theta':theta}
 
 
 def drawRadarChart(data, kind, filebase, params, color):
     """Draw the radar plots"""
     tmp_lin = data * 0
     tmp_log = data * 0
     for key in data.keys():
         if data[key] >= 1:
            tmp_log[key] = np.log10(data[key])
         else:
            tmp_lin[key] = (data[key])
     params['ax'].plot(params['theta'], tmp_log, color='b', label=filebase+"_"+kind+"_log")
     params['ax'].plot(params['theta'], tmp_lin, color='r', label=filebase+"_"+kind+"_linear")
     params['ax'].legend(loc='best', bbox_to_anchor=(1.4,1.2))
     params['ax'].set_rlim((0, np.ceil(max(tmp_log))))
 
 def multiAppBarChartSettings(ax, plt, index, width, n, tmp, s):
     ax.set_yscale('log')
     ax.legend()
     ax.set_xticks(index + width * n / 2)
     ax.set_xticklabels(tmp[s]['Total'].keys(), rotation=50, horizontalalignment='right')
     plt.xlabel("OMP Constructs")
     plt.ylabel(statProperties[s][0])
     plt.title(statProperties[s][1])
     plt.tight_layout()
 
 def derivedTimerStats(data):
     stats = {}
     for key in data.keys():
         if key == 'OMP_worker_thread_life':
             totalRuntime = data['OMP_worker_thread_life']
         elif key in ('FOR_static_iterations', 'OMP_PARALLEL_args',
                      'OMP_set_numthreads', 'FOR_dynamic_iterations'):
             break
         else:
             stats[key] = 100 * data[key] / totalRuntime
     return stats
 
 def compPie(data):
     compKeys = {}
     nonCompKeys = {}
     for key in data.keys():
         if key in ('OMP_critical', 'OMP_single', 'OMP_serial',
                    'OMP_parallel', 'OMP_master', 'OMP_task_immediate',
                    'OMP_task_taskwait', 'OMP_task_taskyield', 'OMP_task_taskgroup',
                    'OMP_task_join_bar', 'OMP_task_plain_bar', 'OMP_task_taskyield'):
             compKeys[key] = data[key]
         else:
             nonCompKeys[key] = data[key]
-    print "comp keys:", compKeys, "\n\n non comp keys:", nonCompKeys
+    print("comp keys:", compKeys, "\n\n non comp keys:", nonCompKeys)
     return [compKeys, nonCompKeys]
 
 def drawMainPie(data, filebase, colors):
     sizes = [sum(data[0].values()), sum(data[1].values())]
     explode = [0,0]
     labels = ["Compute - " + "%.2f" % sizes[0], "Non Compute - " + "%.2f" % sizes[1]]
     patches = plt.pie(sizes, explode, colors=colors, startangle=90)
     plt.title("Time Division")
     plt.axis('equal')
     plt.legend(patches[0], labels, loc='best', bbox_to_anchor=(-0.1,1), fontsize=16)
     plt.savefig(filebase+"_main_pie", bbox_inches='tight')
 
 def drawSubPie(data, tag, filebase, colors):
     explode = []
-    labels = data.keys()
-    sizes = data.values()
+    labels = list(data.keys())
+    sizes = list(data.values())
     total = sum(sizes)
     percent = []
     for i in range(len(sizes)):
         explode.append(0)
         percent.append(100 * sizes[i] / total)
         labels[i] = labels[i] + " - %.2f" % percent[i]
     patches = plt.pie(sizes, explode=explode, colors=colors, startangle=90)
     plt.title(tag+"(Percentage of Total:"+" %.2f" % (sum(data.values()))+")")
for s in interestingStats:
             data = tmp[s]['Total']
             """preventing repetition by removing rows similar to Total_OMP_work
                 as Total_OMP_work['Total'] is same as OMP_work['Total']"""
             if s == 'counters':
                 elapsedTime = tmp["timers"]["Mean"]["OMP_worker_thread_life"]
                 normalizeValues(tmp["counters"], "SampleCount",
                     elapsedTime / 1.e9)
                 """Plotting radar charts"""
-                params = setRadarFigure(data.keys())
+                params = setRadarFigure(list(data.keys()))
                 chartType = "radar"
                 drawRadarChart(data, s, filebase, params, colors[n])
                 """radar Charts finish here"""
                 plt.savefig(filebase+"_"+s+"_"+chartType, bbox_inches='tight')
             elif s == 'timers':
-                print "overheads in "+filebase
+                print("overheads in "+filebase)
                 numThreads = tmp[s]['SampleCount']['Total_OMP_parallel']
                 for key in data.keys():
                     if key[0:5] == 'Total':
                         del data[key]
                 stats[filebase] = derivedTimerStats(data)
                 dataSubSet = compPie(stats[filebase])
                 drawMainPie(dataSubSet, filebase, colors)
                 plt.figure(0)
                 drawSubPie(dataSubSet[0], "Computational Time", filebase, colors)
                 plt.figure(1)
                 drawSubPie(dataSubSet[1], "Non Computational Time", filebase, colors)
                 with open('derivedStats_{}.csv'.format(filebase), 'w') as f:
                     f.write('================={}====================\n'.format(filebase))
-                    f.write(pd.DataFrame(stats[filebase].items()).to_csv()+'\n')
+                    f.write(pd.DataFrame(list(stats[filebase].items())).to_csv()+'\n')
             n += 1
     plt.close()
 
 if __name__ == "__main__":
     main()